developer-helpers {yardstick} | R Documentation |
Developer helpers
Description
Helpers to be used alongside check_metric, yardstick_remove_missing and metric summarizers when creating new metrics. See Custom performance metrics for more information.
Usage
dots_to_estimate(data, ...)
get_weights(data, estimator)
finalize_estimator(
x,
estimator = NULL,
metric_class = "default",
call = caller_env()
)
finalize_estimator_internal(
metric_dispatcher,
x,
estimator,
call = caller_env()
)
validate_estimator(estimator, estimator_override = NULL, call = caller_env())
Arguments
data |
A table with truth values as columns and predicted values as rows. |
... |
A set of unquoted column names or one or more
|
estimator |
Either |
x |
The column used to autoselect the estimator. This is generally
the |
metric_class |
A single character of the name of the metric to autoselect
the estimator for. This should match the method name created for
|
call |
The execution environment of a currently
running function, e.g. |
metric_dispatcher |
A simple dummy object with the class provided to
|
estimator_override |
A character vector overriding the default allowed
estimator list of
|
Dots -> Estimate
dots_to_estimate()
is useful with class probability metrics that take
...
rather than estimate
as an argument. It constructs either a single
name if 1 input is provided to ...
or it constructs a quosure where the
expression constructs a matrix of as many columns as are provided to ...
.
These are eventually evaluated in the summarise()
call in
metric-summarizers and evaluate to either a vector or a matrix for
further use in the underlying vector functions.
Weight Calculation
get_weights()
accepts a confusion matrix and an estimator
of type
"macro"
, "micro"
, or "macro_weighted"
and returns the correct weights.
It is useful when creating multiclass metrics.
Estimator Selection
finalize_estimator()
is the engine for auto-selection of estimator
based
on the type of x
. Generally x
is the truth
column. This function
is called from the vector method of your metric.
finalize_estimator_internal()
is an S3 generic that you should extend for
your metric if it does not implement only the following estimator types:
"binary"
, "macro"
, "micro"
, and "macro_weighted"
.
If your metric does support all of these, the default version of
finalize_estimator_internal()
will autoselect estimator
appropriately.
If you need to create a method, it should take the form:
finalize_estimator_internal.metric_name
. Your method for
finalize_estimator_internal()
should do two things:
If
estimator
isNULL
, autoselect theestimator
based on the type ofx
and return a single character for theestimator
.If
estimator
is notNULL
, validate that it is an allowedestimator
for your metric and return it.
If you are using the default for finalize_estimator_internal()
, the
estimator
is selected using the following heuristics:
If
estimator
is notNULL
, it is validated and returned immediately as no auto-selection is needed.If
x
is a:-
factor
- Then"binary"
is returned if it has 2 levels, otherwise"macro"
is returned. -
numeric
- Then"binary"
is returned. -
table
- Then"binary"
is returned if it has 2 columns, otherwise"macro"
is returned. This is useful if you havetable
methods. -
matrix
- Then"macro"
is returned.
-
Estimator Validation
validate_estimator()
is called from your metric specific method of
finalize_estimator_internal()
and ensures that a user provided estimator
is of the right format and is one of the allowed values.
See Also
metric-summarizers check_metric yardstick_remove_missing